The use of surrogate data for nonlinear identification is illustrated using a case study. It is shown how the surrogate data approach can be used to detect nonlinear features in the experimental data, and how this app...
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The use of surrogate data for nonlinear identification is illustrated using a case study. It is shown how the surrogate data approach can be used to detect nonlinear features in the experimental data, and how this approach can be used to determine whether a model is insufficient for describing the detected nonlinear predictability. In order to develop a nonlinear model, the structure of an identified continuous time linear model is used. The developed continuous time nonlinear models are then evaluated, and some deemed adequate for capturing the nonlinear predictability of the data.
This paper addresses the complex issues involved extending prediction intervals for inspection and maintenance of electrical protection equipment on manufacturing lines. Illustrations are based on representative data.
This paper addresses the complex issues involved extending prediction intervals for inspection and maintenance of electrical protection equipment on manufacturing lines. Illustrations are based on representative data.
In this paper, we present a design method for Unknown Inputs Observers used for Actuator and Component Fault Detection and Isolation. Based on the formulation introduced by (Hou and Müller,1994) a more simple sol...
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In this paper, we present a design method for Unknown Inputs Observers used for Actuator and Component Fault Detection and Isolation. Based on the formulation introduced by (Hou and Müller,1994) a more simple solution is put forward to design a set of observers which detects and isolates the failures by the observation of the nonzero occurrence of the residuals. The necessary and sufficient conditions for the existence of these observers are given, and the results are illustrated by a numerical example
algorithms are presented for detection and tracking of multiple clusters of co-ordinated targets. Based on a Markov chain Monte Carlo sampling mechanization, the new algorithms maintain a discrete approximation of the...
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algorithms are presented for detection and tracking of multiple clusters of co-ordinated targets. Based on a Markov chain Monte Carlo sampling mechanization, the new algorithms maintain a discrete approximation of the filtering density of the clusters’ state. The filters’ tracking efficiency is enhanced by incorporating various sampling improvement strategies into the basic Metropolis-Hastings scheme. Thus, an evolutionary stage consisting of two primary steps is introduced: 1) producing a population of different chain realizations, and 2) exchanging genetic material between samples in this population. The performance of the resulting evolutionary filtering algorithms is demonstrated in two different settings. In the first, both group and target properties are estimated whereas in the second, which consists of a very large number of targets, only the clustering structure is maintained.
Consider the problem of loss function minimization when only (possibly noisy) measurements of the loss function are available. In particular, no measurements of the gradient of the loss function are assumed available ...
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Consider the problem of loss function minimization when only (possibly noisy) measurements of the loss function are available. In particular, no measurements of the gradient of the loss function are assumed available (as required in the steepest descent or Newton-Raphson algorithms). Stochastic approximation (SA) algorithms of the multivariate Kiefer-Wolfowitz (finite-difference) form have long been considered for such problems, but with only limited success. The simultaneous perturbation SA (SPSA) algorithm has successfully addressed one of the major shortcomings of those finite-difference SA algorithms by significantly reducing the number of measurements required in many multivariate problems of practical interest. This paper presents a second-order SPSA algorithm that is based on estimating both the loss function gradient and inverse Hessian matrix at each iteration. The aim of this approach is to emulate the acceleration properties associated with deterministic algorithms of Newton-Raphson form, particularly in the terminal phase where the first-order SPSA algorithm slows down in its convergence. This second-order SPSA algorithm requires only three loss function measurements at each iteration, independent of the problem dimension. This paper includes a formal convergence result for this second-order approach.
This paper is an endeavour to address the problem of distributed leader selection in a formation of autonomous agents where the agents do not communicate directly via communication channels. The algorithm that the age...
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This paper is an endeavour to address the problem of distributed leader selection in a formation of autonomous agents where the agents do not communicate directly via communication channels. The algorithm that the agents use to select a leader relies on the agents observing each others’ behaviours. It is shown that the proposed algorithm is terminated, on average, in finite number of step and results in the selection of a leader for the formation. Moreover, It is established that the algorithm has some common elements with an algorithm widely used in data networks, i.e. Slotted Aloha. The application of the algorithm to a formation controlled by a nonlinear control law is studied and some numerical examples are presented to show the general performance of the algorithm.
In this paper, we consider the problem of the accurate estimation of the depth in the specific context of 2D visual servoing. To this aim, we have developed a predictor/estimator pair. This depth will then be used to ...
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In this paper, we consider the problem of the accurate estimation of the depth in the specific context of 2D visual servoing. To this aim, we have developed a predictor/estimator pair. This depth will then be used to solve simultaneously three problems, namely the determination of the interaction matrix, the estimation of the visual features (when the image is temporarily unavailable) and the automatic computation of their reference values. Simulation results validate our work.
Two autopilots for track-keeping of the ship along the defined trajectory are described in the article: a linear quadratic output feedback controller and a cascaded robust controller. Both are using the rudder as the ...
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Two autopilots for track-keeping of the ship along the defined trajectory are described in the article: a linear quadratic output feedback controller and a cascaded robust controller. Both are using the rudder as the only control-actuator. The paper presents mathematical foundations of the problem, filtering techniques, and results of the simulations performed on a material ship model on the Silm Lake.
This paper presents a model based on the rigid water column (RWC) theory to describe the flow and the decay of chlorine in water distribution networks (WDNs), which can be used for developing tools to diagnose leaks a...
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This paper presents a model based on the rigid water column (RWC) theory to describe the flow and the decay of chlorine in water distribution networks (WDNs), which can be used for developing tools to diagnose leaks and estimate chlorine concentrations. The model includes the continuity equation for each node of the network such that i) the relation of the flow rates entering and leaving the nodes is explicit, and ii) the computation of pressures and flow rates can be simultaneously done. The chlorine decay in each node and in each pipeline section of the WDN is predicted from the computed flow rates by using the third order accurate Warming-Kutler-Lomax (WKL) method. At the end of this paper, it is shown that the chlorine decay rate is well predicted by using the WKL method according to a comparison with simulations results obtained by using the EPANET-MSX software. Furthermore, it is shown that several single leak-diagnosis scenarios can be successfully solved by using an improved sensitivity matrix method together with the proposed model.
In the Netherlands, an increasingly used traffic management measure is to prohibit trucks to overtake during peak hours. Real-life assessment of this measure shows mixed results, especially from the viewpoint of a mor...
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In the Netherlands, an increasingly used traffic management measure is to prohibit trucks to overtake during peak hours. Real-life assessment of this measure shows mixed results, especially from the viewpoint of a more efficient roadway use. The aim of this paper is to explain these results. One aspect of the analysis is considering differences in car-following behaviour between mixed-class vehicle pairs. It appears that possible increases in roadway capacity due to overtaking prohibitions for trucks are to a large extent determined by the traffic conditions and traffic flow composition. Secondly, by application of a recently developed macroscopic flow model for multiclass multilane traffic operations, the homogenising effects resulting from trucks on the left roadway lane are discussed.
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